Improving Veterinary Service Efficiency: Optimizing Home Visit Routes for Pet Clinics Using Particle Swarm Optimization Algorithm

High-stress levels can trigger other diseases if emotions are not channeled to reduce the feeling of stress. The stress-releasing activity that is currently trending is keeping animals, especially cats and dogs. Having a pet triggers awareness of the importance of pet care, especially in the context of home visiting services, highlighting the need for increased effectiveness of veterinary services. Determining a short route for veterinarians to visit their patients in a certain area is necessary in planning for pet clinics with home visit services. In the context of home visits by pet clinics, the Traveling Salesman Problem (TSP) is used to determine the most efficient route veterinarians can use when visiting many patients in a certain area by minimizing the total distance traveled to save time. This research uses the Particle Swarm Optimization (PSO) algorithm with 2-opt logic to solve the TSP problem. This TSP optimization was completed by utilizing Google Colab as a machine learning computing medium using Python. The results of optimizing the total distance for the best routes of home visits to pet clinics was 292.81 kilometers. This research can still be developed for more complex routes and have requirements for each route destination, such as visiting time for each patient and distance between destination points. The research is expected to impact the welfare of pets and meet community needs positively.